اخبار و اعلانات
آمار
| تعداد نشریات | 38 |
| تعداد شماره‌ها | 1,240 |
| تعداد مقالات | 8,994 |
| تعداد مشاهده مقاله | 7,846,618 |
| تعداد دریافت فایل اصل مقاله | 4,707,249 |
داشبورد علم داده جمعیتی؛ چیستی، چرایی و چگونگی | ||
| فصلنامه جمعیت و پیشرفت | ||
| مقاله 2، دوره 2، شماره 1 - شماره پیاپی 3، فروردین 1403، صفحه 71-100 اصل مقاله (2.25 M) | ||
| نوع مقاله: مقاله پژوهشی با اصالت | ||
| نویسنده | ||
| یاسر خرمی* | ||
| استادیار، دانشکده پیامبر اعظم (ص)، دانشگاه جامع امام حسین (ع)، تهران، ایران. | ||
| تاریخ دریافت: 19 اردیبهشت 1403، تاریخ پذیرش: 31 تیر 1403 | ||
| چکیده | ||
| استفاده از داده‌هایی که جمعیت‌های انسانی را پوشش می‌دهند به طور فزاینده‌ای برای تحقیق و تصمیم‌گیری در حال افزایش است. با این حال، پرداختن به این داده‌ها بصورت علمی، که مشخصه اختصاصی جمعیت را در تفاوت با سایر انواع داده‌ها دارند، موضوعی است که در عصر علم داده و کلان‌داده، بصورت تخصصی کماکان مغفول مانده است. علم داده‌های جمعیتی بصورت میان رشته‌ای از تجمیع علم داده و علم جمعیت با نگاه تخصصی به داده‌های جمعیتی قابل طرح است. از آنجاییکه پتانسیل کامل داده‌های جمعیت اغلب تنها زمانی قابل دستیابی است که چنین داده‌هایی به پایگاه‌های داده دیگر مرتبط شوند، نگاه کلی‌نگر و اساسی به موضوع علم داده‌های جمعیتی یکی از ملزومات اساسی در حوزه جمعیت است. در این مقاله تلاش شده است تا با بیان چیستی و چرایی این علم، چگونگی پیاده‌سازی معماری مفهومی مبتنی بر کلان‌داده و رایانش ابری با تکیه بر دانش روز حوزه علم داده در قالب مدل پیشنهادی داشبورد مدیریتی علم داده جمعیتی نشان داده شود. | ||
| کلیدواژه‌ها | ||
| داده های جمعیتی؛ رایانش ابری؛ علم داده های جمعیتی؛ علم داده؛ کلان داده؛ مدل داشبورد مدیریتی | ||
| عنوان مقاله [English] | ||
| Population data science dashboard; what, why, and how | ||
| نویسندگان [English] | ||
| Yaser Khorrami | ||
| Assistant Professor, Faculty of the Great Prophet, Imam Hossein University, Tehran, Iran. | ||
| چکیده [English] | ||
| The use of data covering human populations is increasingly used for research and decision-making. However, dealing with these data in a scientific manner, which has the specific characteristics of the population in contrast to other types of data, is a subject that has been neglected in the age of data science and big data. Population data science can be designed as an interdisciplinary form of combining data science and population science with a specialized view of population data. Since the full potential of population data can often only be achieved when such data are linked to other databases, a comprehensive and basic look at the subject of population data science is one of the basic requirements in the field of population. In this article, it has tried to show what and why this science is, how to implement the conceptual architecture based on big data and cloud computing, relying on the current knowledge of the field of data science in the form of a proposed management dashboard model of population data science. | ||
| کلیدواژه‌ها [English] | ||
| Population data, Cloud computing, Population data science, Data science, Big data, Management dashboard model | ||
| مراجع | ||
|
Berntzen, L., Johannessen, M. R., & El-gazzar, R. “Smart Cities, Big Data and Smart Decision-making Understanding Big Data in Smart City Applications”, The Twelfth International Conference on Digital Society and EGovernments, c, 7–13, 2018. Lori S. Ashford, Toshiko Kaneda, and Emmanuel Letouzé, “Demystifying Big Data for Demography and Global Health,” Population Bulletin 76, no. 1, 2022. Subbotin, A., Aref, S. “Brain drains and brain gain in Russia: Analyzing international migration of researchers by discipline using Scopus bibliometric data 1996–2020,” Scientometrics 126, 7875–7900, 2021. (https://doi.org/10.1007/s11192-021-04091-x) McGrail, K., Moran, R., O'Keefe, C., Preen, D., Quan, H., Sanmartin, C., Schull, M., Smith, M., Williams, C., Williamson, T., Wyper, G., Moorin, R., Kum, H.-C., Jones, K., Akbari, A., Bennett, T., Boyd, A., Carinci, F., Cui, X., Denaxas, S., Dougall, N., Ford, D., Kirby, R. S. and Kotelchuck, M. “A Position Statement on Population Data Science:: The science of data about people”, International Journal of Population Data Science, 3(1), 2018 (doi: 10.23889/ijpds.v3i1.415). Sarikaya, A., Correll, M., Bartram, L., Tory, M., & Fisher, D. “What do we talk about when we talk about Dashboards?”, IEEE Transactions on Visualization and Computer Graphics, 3–22, 2019 (https://doi.org/10.5422/fso/9780823226351.003.0001). Maciel, Frederico Brandão Beleza, “Development of a data report structure for Porto City Council: literature review and benchmark analysis on best dashboard design practices”, CPBS - Dissertações de Mestrado / Master Dissertations, 2022. Matheus, R., Janssen, M., & Maheshwari, D. “Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities”, Government Information Quarterly, 37(3), 101284, 2020 (https://doi.org/10.1016/j.giq.2018.01.006). Bohon, S.A. “Demography in the Big Data Revolution: Changing the Culture to Forge New Frontiers”, Popul Res Policy Rev 37, 323–341, 2018 (https://doi.org/10.1007/s11113-018-9464-6). Head, M. G. “A real-time policy dashboard can aid global transparency in the response to coronavirus disease 2019. International Health, 12(5), 373–374, 2020 (https://doi.org/10.1093/inthealth/ihaa039). Arun Mitra, Biju Soman, Gurpreet Singh, “An Interactive Dashboard for Real-Time Analytics and Monitoring of COVID-19 Outbreak in India: A proof of Concept”, In proceedings of the 1st Virtual Conference on Implications of Information and Digital Technologies for Development, 2021 ( Louisa Jorm. “Routinely collected data as a strategic resource for research: priorities for methods and workforce”, Public Health Research Practice, 25 (4), 2015 (https://doi.org/10.17061/phrp2541540). Janssen, M., & Kuk, G. “Big and Open Linked Data (BOLD) in research, policy, and practice”, Journal of Organizational Computing and Electronic Commerce, 26(1–2), 3–13, 2016 (https://doi.org/10.1080/10919392.2015.1124005) Pan, Y., Tian, Y., Liu, X., Gu, D., & Hua, G. “Urban Big Data and the Development of City Intelligence”, Engineering, 2(2), 171–178, 2016 (https://doi.org/10.1016/J.ENG.2016.02.003) David J Hand “Statistical challenges of administrative and transaction data. Journal of the Royal Statistical Society: Series A (Statistics in Society)”, 181 (3):555–605, 2018 (https://doi.org/10.1111/rssa.12315) Kimberlyn M McGrail, Kerina Jones, Ashley Akbari, Tellen D Bennett, Andy Boyd, et al. “A position statement on population data science: The science of data about people”, International Journal of Population Data Science, 3(1), 2018 (https://doi.org/10.23889/ijpds.v3i1.415) Florian Keusch and Frauke Kreuter “Digital trace data: Modes of data collection, applications, and errors at a glance”, In Handbook of Computational Social Science, Vol 1, pages 100–118. Taylor and Francis, 2021 (https://doi.org/10.4324/9781003024583-8) Paul Biemer. Errors and inference. In: Ian Foster, Rayid Ghani, Ron S Jarmin, Frauke Kreuter, and Julia Lane, editors, “Big Data and Social Science”, chapter 10, pages 265–297. CRC Press, Boca Raton, 2017 (https://doi.org/10.1201/9781315368238). Andrew W Brown, Kathryn A Kaiser, and David B Allison. “Issues with data and analyses: Errors, underlying themes, and potential solutions”, Proceedings of the National Academy of Sciences, 115(11):2563–2570, 2018 (https://doi.org/10.1073/pnas.1708279115). Gartner, Gartner- Dashboard Definition, 2021 (https://www.gartner.com/en/information-technology/glossary/dashboard). Winglee M, Valliant R, Scheuren F: “A case study in record linkage”, Survey Methodology, 31 (1): 3-11, 2005. Emery, J., & Boyle, D. “Data linkage. Australian Family Physician”, 46(8), 615–619, 2017 (https://search.informit.org/doi/10.3316/informit.985328596864810). Flack, A. Kemp-Casey, N. Wray, “using linked administrative data in clinical trials: A Guide for Clinical Trialists and Researchers”, Australian Clinical Trials Alliance, Retrieved from the ACTA website www.clinicaltrialsalliance.org.au, 2019. Kelman CW, Bass AJ, Holman CD. “Research Use of Linked Health Data-A Best Practice Protocol”, Aust N Z J Public Health, 26(3):251-5, 2002. Karmel R, Anderson P, Gibson D, Peut A, Duckett S, Wells Y. “Empirical Aspects of Record Linkage Across Multiple Data Sets Using Statistical Linkage Keys: The Experience of the PIAC Cohort Study”, BMC Health Serv Res, 2010. Gill L. “Methods for Automatic Record Matching and Linking and Their Use in National Statistics”, Statistics N, editor. London, 2001. HLG-PCCB (High-level group for partnership, coordination and capacity-building for statistics for the 20130 agenda for sustainable development). Global action plan for sustainable development data. Report, 2016 (https://unstats.un.org/sdgs/files/global-consultation-hlg-1/GAP_HLG-20161021.pdf). Caio Moreno, Ramón Alberto Carrasco, Enrique Herrera-Viedma, “Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organizations”, International Journal of Interactive Multimedia and Artificial Intelligence, 2019. Ruggles, S. “Big microdata for population research. Demography”, 51(1), 287–297, 2014. Baffour, B., King, T. and Valente, P. “The Modern Census: Evolution, Examples and Evaluation”, International Statistical Review, 81: 407-425, 2013 (https://doi.org/10.1111/insr.12036). Peter Christen, and Rainer Schnell, “Thirty-three myths and misconceptions about population data: from data capture and processing to linkage”, International Journal of Population Data Science, 2023. Gartner: Data Management Solutions for Analytics, 2019 (https://www.gartner.com/reviews/customers-choice/datawarehouse-solutio). Gartner: Magic Quadrant for Data Management Solutions for Analytics, 2019 (https://www.gartner.com/doc/3898487/magicquadrant-data-management-solutions). Nikhat Akhtar, Nazia Tabassum, Asif Perwej, Yusuf Perwej “Data analytics and visualization using Tableau utilitarian for COVID-19”, Global Journal of Engineering and Technology Advances, 2020 (https://doi.org/10.30574/gjeta.2020.3.2.0029). | ||
|
آمار تعداد مشاهده مقاله: 122 تعداد دریافت فایل اصل مقاله: 84 |
||